Dominik Ryżko

Multi-agent Architecture for Real-Time Big Data Processing

The paper describes the architecture for processing of Big Data in real-time based on multi-agent system paradigms. The overall approach to processing of offline and online data is presented. Possible applications of the architecture in the area of recommendation system is shown, however it is argued the approach is general purpose.

Distributed Default Logic for Multi-agent System

This paper presents the distributed default logic (DDL), the formalism for multi-agent knowledge representation and reasoning. The approach allows effective location of knowledge in the MAS to perform the distributed default reasoning (DDR). In the distributed environment learning processes provide measures to order default rules, which gives the agent better use of the local and external knowledge.

Emerging Intelligent Technologies in Industry

Intelligent technologies are the essential factors of innovation, and enable the industry to overcome technological limitations and explore the new frontiers. Therefore it is necessary for scientists and practitioners to cooperate and inspire each other, and use the latest research results in creating new designs and products. The idea of this book came out with the industrial workshop organized at the ISMIS conference in Warsaw, 2011. The book covers several applications of emerging, intelligent technologies in various branches of the industry. The contributions describe modern intelligent tools, algorithms and architectures, which have the potential to solve real problems, experienced by practitioners in various industry sectors. We hope this volume will show new directions for cooperation between science and industry and will facilitate efficient transfer of knowledge in the area of intelligent information systems.

Knowledge sharing in default reasoning based multi-agent systems

This paper brings an idea of a multi-agent system, in which agents use logical programming for reasoning and interaction. To cope with changing environment agents use default rules and default reasoning, which enables them to reason using default and concise knowledge received from other agents (i.e. rules and exceptions). Moreover predicates in the systems are classified according to the degree of trust and while reasoning this classification is taken into account. Concepts based on default theory stratification have also been used.

Integration between Web Services and Multi-Agent Systems with Applications for Multi-commodity Markets

This chapter analyzes possibilities of integration between Web services and multi-agent technology. Efforts of Agents and Web Services Interoperability Working Group (AWSI WG) are described, which is focused on Web Services and FIPA (Foundation for Intelligent Physical Agents) interoperability. A hybrid architecture for conducting trade in a multi-commodity markets, which is based on multi-agent approach combined with the best practices and standards of the Service Oriented Architecture is also proposed. The architecture allows the trade to be conducted by large parties with well structured and defined offer as well as smaller entities, which operate on smaller scale and do not have resources to build full featured catalogues.

Learning of Defaults by Agents in a Distributed Multi-Agent System Environment

The paper introduces a novel approach to machine learning in a multi-agents system. A distributed version of Inductive Logic Programming is used, which allows agents to construct new rules based on knowledge and examples, which are available to different memebrs of the system. The learning process is performed in two phases – first locally by each agent and then on the global level while reasoning.

Customer Experience Management Architecture for Enhancing Corporate Customer Centric Capabilities

The paper reports results of theoretical research aimed at devising methods for representation of experience in computer systems for the purpose of implementing Customer Experience Management (CEM) systems. The paper introduces a novel architecture for modelling customer experience with respect to company, brand, product and its relation to consumer decisions. A process for customer experience approximation is proposed and links to customer decision making are mapped. Experience gaining by a customer has been modelled as a learning process which opens up the way for applying various machine learning algorithms to customer experience emulation. Each customer is represented as an intelligent agent, which reflects the distributed nature of the problem and allows for autonomy of its elements. It is shown how the architecture can be utilised with existing resources e.g. Customer Relationship Management systems as a source of data for CEM.

Exchange of Knowledge and Learning in Default Reasoning Based Agent Systems

The paper is a continuation of [1]. In [1] we have presented an idea of multi-agent system (MAS), in which agent knowledge is presented in the form default theories [12], expressed by default rules and exceptions. To cope with missing knowledge, each agent can communicate to others and receive knowledge components needed for (default) reasoning. Such approach requires from the agents permanent acquiring and verification of both environmental and domain knowledge. In this paper we concentrate on agents learning capabilities. An extension of EBL learning method is proposed.

Automatic Clustering Methods of Offers in an E-Commerce Marketplace

This work describes fully automatic clustering methods of offers in an e-commerce marketplace. Three different grouping approaches are proposed. We also designed and applied quality measures of clustering based on user-generated events. We assessed the proposed methods of clustering and compared them.

A Framework for Experience Representation

This paper proposes a framework for representing the subjective dimension of experience within artificial systems, in particular information systems that emulate behaviour of natural agents. As opposed to the mainstream approach in knowledge engineering it is proposed that knowledge is not equal to experience, in the sense that experience is a broader term which encapsulates both knowledge and subjective, affective component of experience and as such can be represented in formal systems, which has not been so far properly addressed by knowledge representation theories. We show also how our work could enhance the mainstream approach to modelling rational agency with BDI framework.